How to estimate the accuracy upper limit of any CNN model over a computer vision classification task
There is no easy rule for this. You can use transfer learning to select a model that works well on image classification. However the accuracy you achieve will be highly dependent on your training set.  If your training set is "similar" in quantity and quality to what was used  for the accuracy achieved by the transfer learning model in some application you have a reasonable chance of coming close to that accuracy. By similar is mean roughly the same number of images per class. By quality I mean what percentage of the pixels in the images that are occupied by the "region of interest -ROI), same level of noise in the images etc. Also it depends on the nature of the classes. If the classes are widely different (elephants vs trees) the accuracy should be higher than if you are try to classify closely related images (human faces).